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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Jangid, Satish Kumar Singh, Alka Pushparajesh, V. Vivekananda Global University Department of Electrical Engineering Jaipur India Noida Institute of Engineering & Technology Computer Application Uttar Pradesh Greater Noida India Faculty of Engineering and Technology Department of Electrical and Elceltronics Engineering Bangalore India

ISBN: (纸本)9798350357769

In the context of smart grids, multi-agent-based applications have garnered a lot of interest recently. Concurrently, integration of IT (information and communication technology) has emerged as a crucial element of the infrastructure of smart grids. This research attempts to give a comprehensive assessment of the technological framework and literature pertaining to the utilization of Multi-Agent Societies (MAS) and the addition of information and communication technologies in electrical power system deployments. In the environment of artificially intelligent grids, agents are thought of as sentient beings that can make decisions and behave in a dynamic and autonomous way by drawing on their innate intellect and past experiences. ICT also makes data and information transmission easier, which is a game-changer in the transition from conventional to smart grids. This article summarizes the multi-agent concept in smart grids and offers a thorough literature study on related topics to elucidate their applications. Additionally, it pays particular attention to Cti standards, particularly IEC 61850, which integrates MAS with ICT. The last section of the paper introduces laboratory framework concepts, which provide insight on the actual use of IEC 61850. © 2023 IEEE.

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An Attention Mechanism Approach for Natural Color Retrieval for Plant Monitoring 20

An Attention Mechanism Approach for Natural Color Retrieval ...

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20th International Conference on electrical engineering/Electronics, computer, Telecommunications and Information technology, ECTI-CON 2023

作者： Chadavadh, Yanatorn Kasetkasem, Teerasit Patrapornnant, Teera Parittotakapron, Sirichai Isshiki, Tsuyoshi Kasetsart University Faculty of Engineering Department of Electrical Engineering Bangkok Thailand National Electronics and Computer-Technology Center Pathunthani Thailand Tokyo Institute of Technology Tokyo Japan

ISBN: (纸本)9798350310467

Even though agriculture practices have been continuously developed with the support of modern technologies, many more improvements can be made to enhance agricultural technologies and businesses. One such technology is the use of specific light color combinations to optimize the growth rate of plants. One obvious drawback is that plants' color will change according to the light color combinations. The light color can fool human eyes and may cause errors when monitoring for plant anomalies. Color correction methods should be applied to help restore the natural plant color with the white light source from the unnaturally colored plant images. Our color correction method uses an application of self-dot-product attention, multi-head attention, and channel attention combined with a U-Net-based model. This proposed method performs the color correction with the input image in the RGB color space in two steps. First, a global transformation network provides the global function that maps the input RGB color vectors from every pixel and produces the corrected RGB color vectors. The global mapping function is the same for all pixels in the image. Next, a local transformation network attempts to correct the local color distortions such as light the flickering of LED light due to the AC power supplier. © 2023 IEEE.

关键词： Deep learning

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Classification of Frequency Disturbance Event in Power Systems Considering Optimal PMU Placement 7

Classification of Frequency Disturbance Event in Power Syste...

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7th IEEE Conference on Energy Internet and Energy System Integration, EI2 2023

作者： Sun, Zhenglong Wang, Xiaoya Levron, Yoash School of Electrical Engineering Northeast Electric Power University Jilin China Technion - Israel Institute of Technology Andrew and Erna Viterbi Faculty of Electrical & Computer Engineering Haifa Israel

ISBN: (纸本)9798350345094

Power system frequency disturbance events are caused by various generation and transmission events, including generator tripping, load disconnection, line tripping, etc. Accurately determining the frequency disturbance events is of great significance for the subsequent reasonable suppression measures and improvement of grid stability. In this paper, we take advantage of the far-reaching development of deep learning to establish a convolutional neural network model applied to the IEEE 39-bus system, in which frequency, voltage, rate of change of frequency, and relative angle shift are converted into images as inputs to the model, to detect the events accurately and directly;furthermore, we propose a greedy algorithm embedded in the optimized arrangement of PMUs trained by a deep learning model, which is based on the optimally ranked PMU node feature data as inputs in the context of partial observability. PMU node feature data as input to obtain the most suitable PMU placement location under partial observability, and ultimately the FDEs classification is achieved accurately, quickly, and economically. © 2023 IEEE.

关键词： Neural network models

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Machine Speech Chain with Emotion Recognition 11

Machine Speech Chain with Emotion Recognition

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11th International Conference on Advanced Informatics: Concept, Theory and Application, ICAICTA 2024

作者： Naufal, Akeyla Pradia Lestari, Dessi Puji Purwarianti, Ayu Azizah, Kurniawati Tanaya, Dipta Sakti, Sakriani School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia University of Indonesia Faculty of Computer Science Depok Indonesia Graduate School of Information Science Nara Institute of Science and Technology Nara Japan

ISBN: (纸本)9798331520311

Developing natural speech recognition and speech synthesis systems requires speech data that authentically represents real emotions. However, this type of data is often challenging to obtain. Machine speech chain offers a solution to this challenge by using unpaired data to continue training models initially trained with paired data. Given the relative abundance of unpaired data compared to paired data, machine speech chain can be instrumental in recognizing emotions in speech where training data is limited. This study investigates the application of machine speech chain in speech emotion recognition and speech recognition of emotional speech. Our findings indicate that a model trained with 50% paired neutral emotion speech data and 22% paired non-neutral emotional speech data shows a reduction in Character Error Rate (CER) from 37.55% to 34.52% when further trained with unpaired neutral emotion speech data. The CER further decreases to 33.75% when additionally trained with combined unpaired speech data. The accuracy of recognizing non-neutral emotions ranged from 2.18% to 53.51 %, though the F1 score fluctuated, increasing by up to 20.6% and decreasing by up to 23.4%. These results suggest that the model demonstrates a bias towards the majority class, as reflected by the values of the two metrics. © 2024 IEEE.

关键词： Chains

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Transient stability enhancement of Beles to Bahir Dar transmission line using adaptive neuro-fuzzy-based unified power flow controller

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Journal of electrical Systems and Information technology 2024年第1期11卷 1-18页

作者： Alemu, Yechale Amogne Yetayew, Tefera Terefe Mossie, Molla Addisu Faculty of Electrical and Computer Engineering Woldia Institute of Technology (WiT) Woldia University Woldia Ethiopia Faculty of Electrical and Computer Engineering Bahir Dar Institute of Technology (BiT) Bahir Dar University Bahir Dar Ethiopia

A power system is a complex, nonlinear, and dynamic system, with operating parameters that change over time. Because of the complexity of big load lines and faults, high-voltage transmission power systems are usually vulnerable to transient instability concerns, resulting in power losses and increased voltage variation. This issue has the potential to cause catastrophic events such as cascade failure or widespread blackouts. This problem affects Ethiopia's high-voltage transmission power grid in the northwest area. Because of the increased demand for electrical energy, transmission lines' maximum carrying capacity should be enhanced to maintain a secure and uninterrupted power supply to consumers. To address the problem, ANFIS-based UPFC is used for high-voltage transmission lines. This device was chosen as the ideal alternative due to its capacity to rapidly correct reactive power on high-voltage transmission networks. The PSO algorithm was used to determine the best location for the UPFC. The ANFIS controller receives voltage error and rate of change of voltage error as inputs. Seventy percentage of the retrieved data are used for ANFIS training and 30% for ANFIS testing. To show the performance of the proposed controller, three-phase ground faults are used from a severity perspective. When a three-phase ground fault occurs at the midpoint of the Beles to Bahir Dar transmission line, for instance, the settling time of rotor angle deviation, rotor speed, rotor speed deviation, and output active power of synchronous generators using ANFIS-based UPFC is reduced by 70.58%, 37.75%, 37.75%, and 43.75%, respectively, compared to a system without UPFC. At peak load, PI-based UPFC and ANFIS-based UPFC reduce active power loss by 51.25% and 71.50%, respectively, compared to a system without UPFC on the Beles to Bahir Dar transmission line. In terms of percentage overshoot and settling time, ANFIS-based UPFC outperforms PI-based UPFC for transient stability enhancement.

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The Impact of the Digital Transformation of Education on Literacy 7

The Impact of the Digital Transformation of Education on Lit...

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7th IEEE International Conference and Workshop Obuda on electrical and Power engineering, CANDO-EPE 2024

作者： Farkas, Imre Kovari, Attila Rajcsanyi-Molnar, Monika Institute of Computer Engineering University of Dunaujvaros Department of Computer Systems and Control Technology Dunaujvaros Hungary Eszterházy Károly Catholic University Institute of Digital Technology Faculty of Computer Science Eger Hungary Obuda University Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Budapest Hungary John Von Neumann University GAMF Faculty of Engineering and Computer Science Kecskemét Hungary Institute of Social Sciences University of Dunaujvaros Department of Economics and Management Dunaujvaros Hungary

ISBN: (纸本)9798331531119

During the COVID19 epidemic, people of all ages from all walks of life around the world have become inevitably familiar with and almost dependent on the digital tools of the age and the opportunities they offer. A change had to take place very quickly that was unprecedented. This process has also affected education. During the time of the reading lock-in, there was a proliferation of digital curricular content. The second time around, online education came to the fore, thanks to less or well-functioning online platforms, and has since become part of everyday teaching. What is the effectiveness and usefulness of sharing knowledge in this way? Is literacy still needed at all? Can online and hybrid (a controlled mix of face-to-face and online) education replace the well-established contact and face-to-face education? The study show that the respondents believe that online education has had an impact on literacy and that it is perceived to have a positive impact. The results indicate that online education has a positive impact on the literacy levels of students, as it facilitates knowledge acquisition and enhances digital literacy skills. © 2024 IEEE.

关键词： digital education digital literacy digital-informatics literacy

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The Emergence of Artificial Intelligence in Education and its Impact on Individual Literacy in Higher Education 7

The Emergence of Artificial Intelligence in Education and it...

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7th IEEE International Conference and Workshop Obuda on electrical and Power engineering, CANDO-EPE 2024

作者： Farkas, Imre Kovari, Attila Rajcsanyi-Molnar, Monika Institute of Computer Engineering University of Dunaujvaros Department of Computer Systems and Control Technology Dunaujvaros Hungary Eszterházy Károly Catholic University Institute of Digital Technology Faculty of Computer Science Eger Hungary Institute of Electronics and Communication Systems Kandó Kalmán Faculty of Electrical Engineering Obuda University Budapest Hungary John Von Neumann University GAMF Faculty of Engineering and Computer Science Kecskemét Hungary Institute of Social Sciences University of Dunaujvaros Department of Economics and Management Dunaujvaros Hungary

ISBN: (纸本)9798331531119

In the last three years, a new partner has emerged for teachers and educators in the field of education. The mushrooming of applications based on large language models has greatly shaped the educational development fields of the present era. The use of various AI applications has become commonplace among students and teachers alike. Many questions are being raised by researchers in this field. What does the rapid development of AI applications bring to the field of pedagogy? Should the products of such applications be compared with human performance? The form presented in this thesis seeks to answer similar questions. What is the impact of frequent use of these applications on the literacy level of individuals? In which areas do students tend to use these applications? The results indicate that half of the students believe their literacy levels will decrease with the mass emergence of AI applications, while 36% feel it will not change. However, 88 % of students have already dealt with AI-based applications, with 64 % using them several times or more, suggesting a high level of integration into their educational processes. © 2024 IEEE.

关键词： Students

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A Novel Approach on Advanced Fruit Disease Identification Using RCNN

A Novel Approach on Advanced Fruit Disease Identification Us...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Kumar, R. Boopathi, P. Rajan, M. Sundar Bangalore India Shri Nehru College of Arts and Science Department of Computer Science Tamil Nadu Coimbatore India Arba Minch Institute of Technology Arba Minch University Faculty of Electrical and Computer Engineering Ethiopia

ISBN: (纸本)9798331509828

The early diagnosis of diseases in fruits holds immense importance for agricultural industries, as it directly impacts production quality and quantity. This study introduces a novel approach utilizing Recursive Convolutional Neural Network (RCNN) for automated citrus plant disease detection. Through a comprehensive methodology involving data collection, image pre-processing, segmentation, feature extraction using Linear Principal Component Analysis (LPCA), and classification, the proposed RCNN algorithm showcases promising results. Experimental evaluation demonstrates the efficacy of the RCNN-based approach, with precision averaging at 0.929, recall at 0.946, accuracy at 0.945, F-measure at 0.943, and minimal time complexity. Compared to traditional manual inspection methods, RCNN offers a more accurate and efficient solution, significantly reducing the burden on farmers and experts while enhancing fruit disease detection capabilities. Overall, the proposed RCNN-based approach presents a significant advancement in fruit disease identification, offering precise and timely detection to mitigate agricultural losses. With its superior performance metrics and streamlined process, RCNN stands as a promising tool for revolutionizing fruit disease diagnosis and improving agricultural productivity. © 2025 IEEE.

关键词： Convolutional neural networks

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A Compact Substrate Integrated Waveguide-Based Multiband Antenna with Seamless Integration for UAV-Based Agricultural Applications

A Compact Substrate Integrated Waveguide-Based Multiband Ant...

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2023 IEEE International Conference on electrical, Electronics, Communication and computers, ELEXCOM 2023

作者： Agrawal, Meha Yadav, Ravi Agrawal, Anjali Indian Institute of Information Technology Design and Manufacturing Department of Electronics and Communication Jabalpur India Technion Israel Institute of Technology Faculty of Electrical and Computer Engineering Haifa Israel

ISBN: (纸本)9798350305111

Unmanned Aerial Vehicles (UAVs) have revolutionized agriculture by enabling remote monitoring, data collection, and precision farming. To ensure reliable wireless communication between UAVs and ground-based systems, compact multiband antennas with seamless integration are crucial. This paper introduces a Compact Substrate Integrated Waveguide based Multiband Antenna (CSMA) designed for UAV based agricultural applications. The CSMA operates across multiple frequency bands, including 5.85 GHz, 7.9 GHz, and 9.8 GHz for crop growth monitoring, soil moisture monitoring, and pest monitoring. The compact size and lightweight design by utilizing Substrate Integrated Waveguide (SIW) enable integration onto UAV platforms without compromising flight performance. The cavity backed SIW structure allows unified integration without causing any interference from the ground plane of the antenna. The CSMA comprises a ring-shaped slot along with two parasitic rectangular slots for achieving the multiband response. An extra shorting via is also positioned inside the SIW cavity to reduce the radiation nulls. Thus, unidirectional and stable broadside radiation is achieved with a realized gain of 6.65 dBi, 5.0 dBi, and 5.3 dBi at 5.85 GHz, 7.9 GHz, and 9.8 GHz, respectively with overall radiation efficiency greater than 94%. The antenna design addresses wireless communication needs in agricultural settings, using UAV platforms. © 2023 IEEE.

关键词： Substrate integrated waveguides

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Centroid-based clustering validity: method and application to quantification of optimal cluster-data space

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Soft Computing 2024年第19期28卷 10853-10872页

作者： Nguyen, Sy Dzung Laboratory for Computational Mechatronics Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh Viet Nam Faculty of Mechanical - Electrical and Computer Engineering School of Technology Van Lang University Ho Chi Minh Viet Nam

Evaluation of clustering validity to set up an optimal cluster-data space (CDS) is a vital task in many fields related to data mining. Almost existing clustering validity indexes (CVIs) lack stability due to being too sensitive to noise, especially impulse noise. Here, we (1) propose a new CVI named DzI (Dzung Index) or fRisk2 using analysis of fuzzy-set-based accumulated risk degree (FARD), and (2) present a new algorithm named fRisk2-bA for determining the optimal number of data clusters. It is a method of evaluation of the centroid-based fuzzy clustering validity. In essence, the fRisk2 still focuses on enhancing the data compression in each cluster and expanding the separation between cluster centroids. However, these features are exploited indirectly through FARD. As a result, the proposed method not only can avoid the difficulties of the traditional ones relying on the compression and separation properties directly but also can distill better local and global attributes in the data distribution to estimate the CDS more fully. Along with the proved theory basis, surveys, including the ones based on noisy datasets from measurements, showed the compared advantages of fRisk2 as follows. (1) The accuracy, stability, and convergence of the fRisk2 are outstanding. (2) Its total calculating cost is lower than the other surveyed CVIs. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Fuzzy clustering

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